Tuning an AdaBoost regressor

The important parameters to vary in an AdaBoost regressor are learning_rate and loss. As with the previous algorithms, we will perform a randomized parameter search to find the best scores that the algorithm can do.

How to do it...

1. Import the algorithm and randomized grid search. Try a randomized parameter distribution:

from sklearn.ensemble import AdaBoostRegressor
from sklearn.model_selection import RandomizedSearchCV

param_dist = {
 'n_estimators': [50, 100],
 'learning_rate' : [0.01,0.05,0.1,0.3,1],
 'loss' : ['linear', 'square', 'exponential']
 }

pre_gs_inst = RandomizedSearchCV(AdaBoostRegressor(),
 param_distributions = param_dist,
 cv...